ArticleRespiratory research2026
Ultra-high-resolution synchrotron phase-contrast CT enables microstructural pulmonary imaging at clinical dose levels.
Article in Respiratory research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundUltra-high-resolution propagation-based synchrotron phase-contrast CT is an emerging technique for lung imaging. However, its feasibility and diagnostic potential at radiation doses comparable to those used in standard clinical procedures has yet to be established. This study aims to evaluate the performance of phase-contrast CT in comparison with state-of-the-art high-resolution multislice CT and bronchoscopy, and to validate its diagnostic accuracy histologically using porcine and, for the first time, human lung specimens.
methodsPhase-contrast CT experiments were conducted at the Italian synchrotron using lung specimens mounted in a custom-made anthropomorphic chest phantom. Imaging utilized two photon-counting detectors under various acquisition settings, followed by artificial intelligence-based denoising. Sequential imaging by phase-contrast CT, multislice CT, and bronchoscopy was performed prior to formaldehyde vapor fixation and histological dissection. Image quality was assessed quantitatively (contrast-to-noise ratio, edge sharpness, power spectra) and qualitatively via radiological scoring across 14 criteria.
resultsPhase-contrast CT achieved effective pixel sizes of 0.067 mm (Hydra detector) and 0.038 mm (LAMBDA detector), at radiation doses near full-dose multislice CT (≈ 12 mGy). Denoising improved contrast without major loss of edge sharpness. Radiological scoring showed phase-contrast CT outperformed multislice CT in visualizing peripheral airways and fine parenchymal structures. Histological validation confirmed imaging accuracy. Limitations from source spot size (≈ 200 μm) were noted but did not prevent significant diagnostic improvements.
conclusionsPhase-contrast CT, combined with artificial intelligence-based denoising, offers detailed, non-invasive imaging of lung microstructures at clinically relevant radiation doses. It complements multislice CT, holds potential for clinical adoption in advanced pulmonary diagnostics, and may reduce reliance on invasive biopsies.
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